

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


__snake_case				:      Optional[int]   		=			r"""
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `\" / \"`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `\" // \"`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `\"train\"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `\"compressed\"`)
            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and
            `\"compressed\"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
"""



@add_start_docstrings(a_							)
class        A__(a_							):
						"""simple docstring"""






						_A    :				Tuple       					=			'''rag'''
						_A    :				List[str]       					=			True





						def __init__(    self  ,      _lowercase=None  ,      _lowercase=True  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=" / "  ,      _lowercase=" // "  ,      _lowercase=5  ,      _lowercase=300  ,      _lowercase=768  ,      _lowercase=8  ,      _lowercase="wiki_dpr"  ,      _lowercase="train"  ,      _lowercase="compressed"  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=False  ,      _lowercase=False  ,      _lowercase=0.0  ,      _lowercase=True  ,      _lowercase=False  ,      _lowercase=False  ,      _lowercase=False  ,      _lowercase=True  ,      _lowercase=None  ,      **_lowercase  ,      )     -> str:
									super().__init__(
									    bos_token_id=_lowercase  ,      pad_token_id=_lowercase  ,      eos_token_id=_lowercase  ,      decoder_start_token_id=_lowercase  ,      forced_eos_token_id=_lowercase  ,      is_encoder_decoder=_lowercase  ,      prefix=_lowercase  ,      vocab_size=_lowercase  ,      **_lowercase  ,      )
									assert (
									    "question_encoder" in kwargs and "generator" in kwargs
									), "Config has to be initialized with question_encoder and generator config"
									a_						:						Any 							= kwargs.pop("""question_encoder"""    )
									a_						:						Any 							= question_encoder_config.pop("""model_type"""    )
									a_						:						Dict 							= kwargs.pop("""generator"""    )
									a_						:						List[str] 							= decoder_config.pop("""model_type"""    )

									from ..auto.configuration_auto import AutoConfig

									a_						:						Any 							= AutoConfig.for_model(_lowercase  ,      **_lowercase    )
									a_						:						Optional[int] 							= AutoConfig.for_model(_lowercase  ,      **_lowercase    )

									a_						:						str 							= reduce_loss
									a_						:						Optional[int] 							= label_smoothing
									a_						:						Tuple 							= exclude_bos_score
									a_						:						List[Any] 							= do_marginalize

									a_						:						List[Any] 							= title_sep
									a_						:						int 							= doc_sep
									a_						:						Optional[Any] 							= n_docs
									a_						:						str 							= max_combined_length

									a_						:						int 							= dataset
									a_						:						Union[str, Any] 							= dataset_split
									a_						:						Union[str, Any] 							= index_name

									a_						:						Any 							= retrieval_vector_size
									a_						:						Dict 							= retrieval_batch_size
									a_						:						Tuple 							= passages_path
									a_						:						Any 							= index_path
									a_						:						List[Any] 							= use_dummy_dataset

									a_						:						Tuple 							= output_retrieved

									a_						:						Dict 							= do_deduplication

									a_						:						Optional[int] 							= use_cache

									if self.forced_eos_token_id is None:
												a_						:						Any 							= getattr(self.generator  ,      """forced_eos_token_id"""  ,      _lowercase    )





						@classmethod
						def 			UpperCamelCase__					(    cls  ,      _lowercase  ,      _lowercase  ,      **_lowercase    )     -> PretrainedConfig:
									return cls(question_encoder=question_encoder_config.to_dict()  ,      generator=generator_config.to_dict()  ,      **_lowercase    )





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						str 							= copy.deepcopy(self.__dict__    )
									a_						:						Dict 							= self.question_encoder.to_dict()
									a_						:						Any 							= self.generator.to_dict()
									a_						:						Optional[int] 							= self.__class__.model_type
									return output





from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
__snake_case				:      Optional[Any]   		=			10


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			for i in range(a__  ,	a__):
						if array[i] == target:
									return i
			return -1


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						Any 							= 0
			a_						:						List[str] 							= len(a__)
			while left <= right:
						if right - left < precision:
									return lin_search(a__  ,	a__  ,	a__  ,	a__)

						a_						:						Dict 							= (left + right) // 3 + 1
						a_						:						Optional[Any] 							= 2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									a_						:						int 							= one_third - 1
						elif array[two_third] < target:
									a_						:						Any 							= two_third + 1

						else:
									a_						:						Optional[int] 							= one_third + 1
									a_						:						int 							= two_third - 1
			else:
						return -1


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__  ,	a__):

			'''simple docstring'''



			if left < right:
						if right - left < precision:
									return lin_search(a__  ,	a__  ,	a__  ,	a__)
						a_						:						Optional[int] 							= (left + right) // 3 + 1
						a_						:						Union[str, Any] 							= 2 * (left + right) // 3 + 1

						if array[one_third] == target:
									return one_third
						elif array[two_third] == target:
									return two_third

						elif target < array[one_third]:
									return rec_ternary_search(a__  ,	one_third - 1  ,	a__  ,	a__)
						elif array[two_third] < target:
									return rec_ternary_search(two_third + 1  ,	a__  ,	a__  ,	a__)
						else:
									return rec_ternary_search(one_third + 1  ,	two_third - 1  ,	a__  ,	a__)
			else:
						return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	__snake_case				:      Optional[int]   		=			input("""Enter numbers separated by comma:\n""").strip()
	__snake_case				:      Optional[int]   		=			[int(item.strip()) for item in user_input.split(""",""")]
	assert collection == sorted(collection), F"List must be ordered.\n{collection}."
	__snake_case				:      int   		=			int(input("""Enter the number to be found in the list:\n""").strip())
	__snake_case				:      List[Any]   		=			ite_ternary_search(collection, target)
	__snake_case				:      Optional[int]   		=			rec_ternary_search(0, len(collection) - 1, collection, target)
	if resulta != -1:
		print(F"""Iterative search: {target} found at positions: {resulta}""")
		print(F"""Recursive search: {target} found at positions: {resulta}""")
	else:
		print("""Not found""")


